What Does Predictive Analytics Predict, and How Well Does It Do?

Predictive analytics does just what it sounds like: it analyzes data to figure out what might happen in the future. As with most predictions, it’s never 100% correct, but big data and artificial intelligence are making it a lot more accurate.

While it was once a relatively specialized branch of mathematics and computer science, new predictive technologies are more accessible and easily applicable: businesses use it on customers, researchers use it on diseases, advertising agencies use it to target consumers, banks use it to prevent fraud, and the list goes on. So how does predictive analytics really work, what does it predict, and how reliable are its forecasts?

Identify important factors: day of the week, time of day, weather, frequency of delays, etc.

Create and “train” a model: try to figure out how each factor has historically influenced driving time.

Plug in your current information and get the result: on a warm Monday at 17:30, your drive will take you thirty minutes.

That’s a simple example, but if you’ve ever taken a look at Google Maps’ traffic predictions, you’ve used something like this. How accurate it is depends on the quality of historical and real-time data that’s available, but it can almost always make a pretty close guess, which is what predictive analytics is all about.

What does it predict?

Predictive analytics is being productively used in medical research, finance, manufacturing, supply chains, and elsewhere, but one of the most profitable applications for this technology is to analyze and predict customer behavior. If you’ve ever wondered why your data is such a precious commodity, this is one of the main reasons. With access to large amounts of historical user data, it’s a lot easier for companies to figure out how they can press consumers’ buttons.

In healthcare and medicine, predictive analytics is being used mostly to optimize treatments and find new ways to fight diseases. By analyzing historical patient data, hospitals can reduce the number of patients who need to come back, create more personalized treatment plans and get more accurate risk assessments. Predictive analytics models are also important for disease research, using data generated by patients and populations to identify risk factors, treatment results, and more.

Applications in finance are similarly focused around risk – specifically, who is a safe bet for a loan or an account? Applying predictive analytics can help financial institutions identify people who are at high risk for default and flag fraud activity more effectively.

But no industry is quite as enthused about predictive analytics as retail and advertising are. Imagine if you could watch your customer’s every move, feed it into a massive database, and analyze it for patterns. You could find out who is most likely to stop using your service, what makes people keep using your product, who is most likely to react to certain ads, who to target with your campaigns – all with data that can be updated and analyzed in real time.

How accurate are these predictions?

There is no single answer to this question, since every model is different. The quality of the data, the methods used to analyze it, and a host of other factors all play into how accurate the predictions can be. Predictive analytics don’t get it right all the time, but thanks to advances in big data and artificial intelligence, they’re getting it right more of the time.

The thing that makes big data “big” isn’t necessarily how much of it there is, but how effectively large amounts of it can be processed. Much of statistics has historically been based on making guesses about populations based on samples drawn from those populations, which adds a layer of uncertainty.

Big data tools, though, make it possible to use a lot more of the available data to make predictions, which makes them much more likely to be correct. Predictive analytics already does a pretty good job serving people ads and figuring out commute times, and it’s only going to be more effective in the future.

Big (bad?) data

How do you make good decisions? For most of human history, we’ve used our brains to process whatever inputs are available and act accordingly. Our decisions have always been tainted by a lack of accurate information, a limited ability to identify patterns, and any number of biases.

A well-made algorithm with a big dataset, though, doesn’t have that problem, and the ability to offload a lot of our mental labor to machines is a big step forward for humanity. Of course, algorithms can be biased, either intentionally or unintentionally, datasets can be corrupted, and predictions about behavior can be used for social control as easily as they can be used for optimizing retail experiences. Making sure our systems develop to be transparent and generally beneficial will have a real impact on the way technology shapes (and predicts) the future.

Hey @dragonmouth please help me out… please ! You’ve post a comment in one of Make Tech Easier article (past), a year or so ago, that dealt with a Big Data topic. Your comment mentioned and reflected to this MTE article of two TV shows this reminded you of, one I’m not recalling one but the other was- ‘Person of Interests’ I seem to recall the MTE article had in it referrences to a thesis or comparences of someone stating about or related to an old thery on Eugenics basically. Can and do you recall this ? Seems to me the image given in the MTE article had something like the Matrix showing of the code like in 1’s and 0’s. Can you give me the title or a link to this MTE article? I’ve went back in MTE articles to 2015, and checked the ‘Way back Web’ – simply nothing found. Like it deleted from the web – didn’t think this was possible… Thank you for your help :)

Hi, Where there is big money to be made, none of this suggests that such of big data analysis is worthless as it may be a highly profitable endeavor. Even a modest increase in the accuracy could be a prize worth winning for scientists, entrepreneurs and governments and the Centers for Disease Control and Prevention (CDC). Big data that interests many companies is what might be call as ‘found data’ from mining their users, of their the digital exhaust of web searches, credit card purchases/payments and mobiles pinging the nearest phone mast or tower.

Worse still, one of the problems to Big Data and Predictive analytics lies with the multiple-comparisons problem in that data is not being shared, as it deals in transparency to the captured and/or shared data pools. Allowing other data miners to figure out how many hypotheses were tested and how many contrary results are languishing in databases somewhere by someone else.

Our communication, leisure and commerce have moved to the internet and the internet has moved into our phones, IoT devices, our cars and even our glasses, life can be recorded and predictive analytics gained in every way that would have been hard to imagine just a decade ago. Every single data point can be captured, making old statistical sampling techniques obsolete. That it is no longer ‘fashionable’ to fret about of what causes what, because statistical correlation of big data tells the miners of what they need to know, and that scientific or statistical models aren’t needed because it’ draws to an end of an era in Theory based findings.

The new found ways of data mining is key and underpins the new internets economy as companies such as Google, Facebook and Amazon all seek in the ways to understand our digital lives through our data exhaust. Then since Edward Snowden’s leaks about the scale and scope of US electronic surveillance, as this has become apparently of interests in the security services that are just as fascinated with what they might learn from our data exhaust of trails as well. All the letter and secret agencies all over the world in their Nations defense claims are probably tapping all internet connected avenues of theirs and other Nations populations.

Were this fails- Because they just didn’t seem interesting enough to publish on the web. Yet, of in all the found data sets and exhaust trails and are rarely being transparent about it to others, nor the webs audience in general. Like as in one Nations governmental spying towards another Nations peoples, or in phishing cases of their populations as know or they’ve found out about.

Amazon, Google, Facebook and Twitter, then Target and Tesco, even Microsoft and Apple – these companies aren’t about to share their Big Data with you or anyone else in a global data base.

Not forgetting the realm of Space where companies and Governments bid in a race to gain, a steak in or holdings of. is It simply greed or driven by greed at this point to the topic of Big Data? What do you think?